Abstract

Current methods for inspecting subgrade compaction quality are time-consuming and destructive. This paper presents an in-situ measurement methodology for rapid detection of subgrade compaction quality using Ultrasonic Pulse Velocity (UPV) and Intelligent Compaction (IC). Field compaction tests, field UPV test, and laboratory tests are performed to construct the heterogeneous datasets. A set of intelligent models are established to estimate the subgrade compactness in terms of the in-time measured UPV. The training strategy is proposed by expanding dataset quantity and dataset dimension to enhance the model performance. The model demonstrates high accuracy in compactness estimation and strong generalization in verification. This contribution can be utilized in engineering applications to effectively detect the compaction quality during the construction.

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